import torch.nn as nn
import torch
class NWKernelRegression(nn.Module):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.w = nn.Parameter(torch.rand((1,), requires_grad=True))
    def forward(self, queries, keys, values):
        # queries和attention_weights的形状为(查询个数, “键-值”对个数)
        queries = queries.repeat_interleave(
            keys.shape[1]).reshape((-1, keys.shape[1]))
        self.attention_weights = nn.functional.softmax(
            -((queries - keys) * self.w)**2 / 2, dim=1) # values的形状为(查询个数, “键-值”对个数)
        return torch.bmm(self.attention_weights.unsqueeze(1),
                         values.unsqueeze(-1)).reshape(-1)


# 数据量 500

# 生成数据集
n_train = 500
x_train, _ = torch.sort(torch.rand(n_train)*5)
def f(x):
    return 2*torch.sin(x)+x**0.8
y_train = f(x_train) + torch.normal(0,0.5,(n_train,))
x_test = torch.arange(0,5,0.1)
y_truth = f(x_test)
n_test = len(x_test)

# X_tile的形状:(n_train,n_train),每一行都包含着相同的训练输入
X_tile = x_train.repeat((n_train, 1))
# Y_tile的形状:(n_train,n_train),每一行都包含着相同的训练输出
Y_tile = y_train.repeat((n_train, 1))
# keys的形状:('n_train','n_train'-1)
keys = X_tile[(1 - torch.eye(n_train)).type(torch.bool)].reshape((n_train, -1))
# values的形状:('n_train','n_train'-1)
values = Y_tile[(1 - torch.eye(n_train)).type(torch.bool)].reshape((n_train, -1))
net = NWKernelRegression()
loss = nn.MSELoss(reduction='none')
trainer = torch.optim.SGD(net.parameters(),lr=0.5)
animator = d2l.Animator(xlabel='epoch',ylabel='loss',xlim=[1,5])
for epoch in range(5):
    trainer.zero_grad()
    l = loss(net(x_train,keys,values),y_train)
    l.sum().backward()
    trainer.step()
    print(f'epoch{epoch+1},loss{float(l.sum()):.6f}')
    animator.add(epoch+1,float(l.sum()))


#keys的形状:(n_test,n_train),每一行包含着相同的训练输入(例如,相同的键)
keys = x_train.repeat((n_test, 1))
# value的形状:(n_test,n_train)
values = y_train.repeat((n_test, 1))
y_hat = net(x_test, keys, values).unsqueeze(1).detach()
plot_kernel_reg(y_hat)